Recognizing frontal face images using Hidden Markov models with one training image per person

Recently many important face recognition systems could deal well with frontal view face images. However few of them work well when there is only one training image per person. We propose an approach to cope with the problem by using 1D discrete hidden Markov model (1D-DHMM). The model training and recognition part were carried out on both vertical and horizontal directions. New way of extracting observations and using observation sequences in recognition is introduced. The Haar wavelet transform was applied to the image to lessen the dimension of the observation vectors. Our experiment results tested on the frontal view AR Face Database show that the proposed method outperforms the PCA, LDA, LFA approaches tested on the same database.